Kolekar Pandurang, Pataskar Abhijeet, Kulkarni-Kale Urmila, Pal Jayanta, Kulkarni Abhijeet
Bioinformatics Centre, Savitribai Phule Pune University (Formerly University of Pune), Pune, Maharashtra, 411 007, India.
Department of Biotechnology, Savitribai Phule Pune University (Formerly University of Pune), Pune, Maharashtra, 411 007, India.
Sci Rep. 2016 Jun 6;6:27436. doi: 10.1038/srep27436.
Cellular mRNAs are predominantly translated in a cap-dependent manner. However, some viral and a subset of cellular mRNAs initiate their translation in a cap-independent manner. This requires presence of a structured RNA element, known as, Internal Ribosome Entry Site (IRES) in their 5' untranslated regions (UTRs). Experimental demonstration of IRES in UTR remains a challenging task. Computational prediction of IRES merely based on sequence and structure conservation is also difficult, particularly for cellular IRES. A web server, IRESPred is developed for prediction of both viral and cellular IRES using Support Vector Machine (SVM). The predictive model was built using 35 features that are based on sequence and structural properties of UTRs and the probabilities of interactions between UTR and small subunit ribosomal proteins (SSRPs). The model was found to have 75.51% accuracy, 75.75% sensitivity, 75.25% specificity, 75.75% precision and Matthews Correlation Coefficient (MCC) of 0.51 in blind testing. IRESPred was found to perform better than the only available viral IRES prediction server, VIPS. The IRESPred server is freely available at http://bioinfo.net.in/IRESPred/.
细胞信使核糖核酸(mRNA)主要以帽依赖性方式进行翻译。然而,一些病毒mRNA和一部分细胞mRNA以帽非依赖性方式起始翻译。这需要在其5'非翻译区(UTR)存在一种被称为内部核糖体进入位点(IRES)的结构化RNA元件。在UTR中对IRES进行实验证明仍然是一项具有挑战性的任务。仅基于序列和结构保守性对IRES进行计算预测也很困难,尤其是对于细胞IRES。开发了一个网络服务器IRESPred,用于使用支持向量机(SVM)预测病毒和细胞IRES。预测模型是使用基于UTR的序列和结构特性以及UTR与小亚基核糖体蛋白(SSRP)之间相互作用概率的35个特征构建的。在盲测中发现该模型具有75.51%的准确率、75.75%的灵敏度、75.25%的特异性、75.75%的精确率和0.51的马修斯相关系数(MCC)。发现IRESPred的性能优于唯一可用的病毒IRES预测服务器VIPS。IRESPred服务器可在http://bioinfo.net.in/IRESPred/免费获取。